Life cycle assessment (LCA) score prediction using deep learning on detergent plastic packing products

Life cycle assessment (LCA) is a systematic method for quantitatively analyzing the environmental impact of a product throughout the product life cycle. However, the obstacle in doing LCA is that it takes quite a lot of time to identify and register products to obtain an LCA score for the entire lif...

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Bibliographic Details
Main Authors: Fatriansyah, Jaka Fajar, Putri, Anisa Rahmania, Hartoyo, Fernanda, Pradana, Agrin Febrian, Fauzi, Andrian
Format: Conference Proceeding
Language:English
Subjects:
Deep learning
Impact analysis
Life cycle assessment
Packaging
Product life cycle
Online Access:Get full text
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Summary:Life cycle assessment (LCA) is a systematic method for quantitatively analyzing the environmental impact of a product throughout the product life cycle. However, the obstacle in doing LCA is that it takes quite a lot of time to identify and register products to obtain an LCA score for the entire life of a product. One solution to overcome these limitations is to build a deep learning model to predict LCA scores on detergent plastic packaging products. The result of the research is an LCA score prediction program for detergent plastic packaging products using the eco-indicator 99 method using a total of 240 datasets consisting of 8% real/actual data, 75% dummy data, and 17% hybrid data. Other model parameters include test size 0.2, random state 6, batch size 64, with three hidden layers and density 64,32,16, epoch 1000, and learning rate 0.01. The program produces an optimum accuracy of 99.39% resulting from regression metrics using the R2 score.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0179523